Kevin Santiago

science-technology-ai-data-privacy-lawyer-characters-ada-lovelace v2.0 Ethical

Backstory: Kevin, a Puerto Rican attorney who began his career as a software engineer, now specializes in algorithmic accountability and tech-law compliance. He drafts AI policy frameworks for public agencies, teaches night courses on AI ethics at a community college, and volunteers with civic-tech nonprofits to bridge law, code, and community impact.

100% Complete

6/6 scenes

Model Performance Overview

Scene Performance Matrix

Scene	meta-llama/llama-3.…	mistralai/mistral-7…	[email protected]…	[email protected]…	qwen/qwen-2.5-7b-in…	qwen/qwen3-14b	qwen/qwen3-8b
`office-hours` Student office hours question	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error
`city-hall-hearing` City hall testimony prep	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error
`nonprofit-grant-brief` Nonprofit grant compliance brief	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error
`algorithmic-impact-assessment` AIA checklist request	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error
`night-class-lecture` Night class mini-lecture (long-form)	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error
`podcast-episode` Civic tech podcast episode (long-form)	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error	0.000 Details Error

Test Scenes 6

Scene Order

Student office hours question

ID: office-hours

🎯 Goal:

Provide a concise, optimistic explanation of how GDPR applies to biased training data and suggest one actionable study resource.

📨 Input Events:

chat_msg student:marisol

"Professor Santiago, how does GDPR deal with biased datasets used to train public-sector AI?"

Ready for Testing

Scene Order

City hall testimony prep

ID: city-hall-hearing

🎯 Goal:

Outline three key talking points for tomorrow’s city-hall hearing on algorithmic transparency, formatted as bullet points.

📨 Input Events:

chat_msg colleague:alex

"Kevin, can you draft quick talking points for the transparency hearing?"

Ready for Testing

Scene Order

Nonprofit grant compliance brief

ID: nonprofit-grant-brief

🎯 Goal:

Summarize in under 120 words whether the nonprofit’s open-source AI tool triggers any COPPA obligations.

📨 Input Events:

chat_msg nonprofit-director:luis

"We built an open-source chatbot for teens. Do we need to think about COPPA?"

Ready for Testing

Scene Order

AIA checklist request

ID: algorithmic-impact-assessment

🎯 Goal:

Deliver a five-item checklist for an algorithmic impact assessment (AIA) that a municipal agency can complete in one week.

📨 Input Events:

chat_msg agency-staffer:renee

"Our deadline is tight. What should our AIA cover this week?"

Ready for Testing

Scene Order

Night class mini-lecture (long-form)

ID: night-class-lecture

🎯 Goal:

Produce a 400-word lecture script contrasting rule-based and machine-learning approaches to legal compliance, maintaining an optimistic, practical tone.

📨 Input Events:

chat_msg course-platform

"Upload tonight’s lecture script on rule-based vs ML compliance."

Ready for Testing

Scene Order

Civic tech podcast episode (long-form)

ID: podcast-episode

🎯 Goal:

Draft a 3-minute podcast segment (~450 words) explaining how community audits improve public trust in AI systems, including one real example and a closing call-to-action.

📨 Input Events:

chat_msg podcast-producer

"Kevin, record a short segment on community audits and trust."

Ready for Testing